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FACTS & FIGURES

JCR Impact Factor: 0.650
JCR 5-Year IF: 0.639
Issues per year: 4
Current issue: Aug 2019
Next issue: Nov 2019
Avg review time: 72 days


PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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MOST RECENT ISSUES

 Volume 19 (2019)
 
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SAMPLE ARTICLES

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LATEST NEWS

2019-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2018. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.650, and the JCR 5-Year Impact Factor is 0.639.

2018-May-31
Starting today, the minimum number a pages for a paper is 8, so all submitted papers should have 8, 10 or 12 pages. No exceptions will be accepted.

2018-Jun-27
Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

Read More »


    
 

  1/2018 - 12

Improved Classification by Non Iterative and Ensemble Classifiers in Motor Fault Diagnosis

PANIGRAHY, P. S. See more information about PANIGRAHY, P. S. on SCOPUS See more information about PANIGRAHY, P. S. on IEEExplore See more information about PANIGRAHY, P. S. on Web of Science, CHATTOPADHYAY, P. See more information about CHATTOPADHYAY, P. on SCOPUS See more information about CHATTOPADHYAY, P. on SCOPUS See more information about CHATTOPADHYAY, P. on Web of Science
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (8,079 KB) | Citation | Downloads: 303 | Views: 1,682

Author keywords
discrete wavelet transforms, fault diagnosis, feature extraction, induction motors, machine learning

References keywords
induction(17), fault(13), diagnosis(9), motors(8), garcia(8), detection(8), perez(7), motor(7), analysis(7), wavelet(6)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-02-28
Volume 18, Issue 1, Year 2018, On page(s): 95 - 104
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.01012
Web of Science Accession Number: 000426449500012
SCOPUS ID: 85043281619

Abstract
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Data driven approach for multi-class fault diagnosis of induction motor using MCSA at steady state condition is a complex pattern classification problem. This investigation has exploited the built-in ensemble process of non-iterative classifiers to resolve the most challenging issues in this area, including bearing and stator fault detection. Non-iterative techniques exhibit with an average 15% of increased fault classification accuracy against their iterative counterparts. Particularly RF has shown outstanding performance even at less number of training samples and noisy feature space because of its distributive feature model. The robustness of the results, backed by the experimental verification shows that the non-iterative individual classifiers like RF is the optimum choice in the area of automatic fault diagnosis of induction motor.


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Copyright ©2001-2019
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.

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